private Node[] Epoch(int t, Drink inputDrink, Node[] nodesArray)
{
BestMatchingNodeMultiThreadedSearcher bestMatchingNodeMultiThreadedSearcher = new BestMatchingNodeMultiThreadedSearcher(nodesArray, processorsCount, inputDrink.FeaturesArray);
int winningNodeIndex = bestMatchingNodeMultiThreadedSearcher.GetBestMatchingNodeIndex();
double neighbourhoodRadius = Radius(t);
double learningRate = LearningRate(t);
potentialsArray[winningNodeIndex] -= (_minNeuronPotential + maxNeuronRestTimeInv);
UpdatePotentialsArray(maxNeuronRestTimeInv);
System.Threading.Tasks.Parallel.ForEach(nodesArray, node =>
{
double theta = Theta(t, nodesArray[winningNodeIndex], node, neighbourhoodRadius);
if (theta > 0)
{
node.AdjustWeights(theta, learningRate, inputDrink.FeaturesArray);
node.DrinkID = inputDrink.ID;
}
});
return(nodesArray);
}
private Node[] Epoch(int t, Drink inputDrink, Node[] nodesArray)
{
BestMatchingNodeMultiThreadedSearcher bestMatchingNodeMultiThreadedSearcher = new BestMatchingNodeMultiThreadedSearcher(nodesArray, processorsCount, inputDrink.FeaturesArray);
int winningNodeIndex = bestMatchingNodeMultiThreadedSearcher.GetBestMatchingNodeIndex();
double neighbourhoodRadius = Radius(t);
double learningRate = LearningRate(t);
potentialsArray[winningNodeIndex] -= (_minNeuronPotential + maxNeuronRestTimeInv);
UpdatePotentialsArray(maxNeuronRestTimeInv);
System.Threading.Tasks.Parallel.ForEach(nodesArray, node =>
{
double theta = Theta(t, nodesArray[winningNodeIndex], node, neighbourhoodRadius);
if (theta > 0)
{
node.AdjustWeights(theta, learningRate, inputDrink.FeaturesArray);
node.DrinkID = inputDrink.ID;
}
});
return nodesArray;
}
